The need to summarize large amounts of data is a recurring problem in the area of streaming data analytics. Nonparametric correlations such as Spearman's rank correlation and Kendall's tau correlation are widely applied in scientific and engineering fields. However, computing nonparametric correlations on the fly for streaming data is problem. Standard batch algorithms are generally too slow to handle real-world big data applications. They also require too much memory because all of the data needs to be stored in memory before processing.